Predicting and adjusting computer functionality to avoid failures

ABSTRACT

In some examples, a processing device can receive prediction data representing a prediction. The processing device can also receive files defining abnormal data-point patterns to be identified in the prediction data. The processing device can identify at least one abnormal data-point pattern in the prediction data by executing customizable program-code in the files. The processing device can determine an override process that corresponds to the at least one abnormal data-point pattern in response to identifying the at least one abnormal data-point pattern in the prediction data. The processing device can execute the override process to generate a corrected version of the prediction data. The processing device can then adjust one or more computer parameters based on the corrected version of the prediction data.

REFERENCE TO RELATED APPLICATION

This claims the benefit of priority under 35 U.S.C. § 119(e) to U.S.Provisional Patent Application No. 62/634,117, filed Feb. 22, 2018, theentirety of which is hereby incorporated by reference herein.

TECHNICAL FIELD

The present disclosure relates generally to diagnostic analysis of acomputer. More specifically, but not by way of limitation, thisdisclosure relates to predicting and adjusting computer functionality toavoid failures.

BACKGROUND

Computers include various software and hardware components that areprone to failure. Such failures can temporarily or permanently affectthe computer's performance and functional capabilities. For example,computer failures can result in lost or corrupted data, a reducedability to process information, a reduced ability to store and retrievedata from memory, memory leaks, and other problems.

SUMMARY

One example of the present disclosure includes a system having aprocessing device and a memory device. The memory device can includeinstructions that are executable by the processing device for causingthe processing device to perform one or more operations. Theinstructions can cause the processing device to receive prediction datarepresenting a prediction, wherein the prediction data forms a timeseries that spans a future time-period. The instructions can cause theprocessing device to receive a plurality of files defining abnormaldata-point patterns to be identified in the prediction data, whereineach file in the plurality of files includes customizable program-codefor identifying a respective abnormal pattern of data-point values inthe prediction data. The instructions can cause the processing device toautomatically identify at least one abnormal data-point pattern in theprediction data by interpreting and executing the customizableprogram-code in the plurality of files. The instructions can cause theprocessing device to automatically determine an override process thatcorresponds to the at least one abnormal data-point pattern in responseto identifying the at least one abnormal data-point pattern in theprediction data. The override process can be automatically determinedusing correlations between the abnormal data-point patterns and overrideprocesses. The override process can involve replacing a value of atleast one data point in the prediction data with another value that isconfigured to mitigate an impact of the at least one abnormal data-pointpattern on the prediction. The instructions can cause the processingdevice to automatically generate a corrected version of the predictiondata in response to determining the override process. The correctedversion of the prediction data can be generated by executing theoverride process. The instructions can cause the processing device toautomatically adjust one or more computer parameters based on thecorrected version of the prediction data.

Another example of the present disclosure includes a non-transitorycomputer-readable medium comprising program code that is executable by aprocessing device for causing the processing device to perform one ormore operations. The program code can cause the processing device toreceive prediction data representing a prediction, wherein theprediction data forms a time series that spans a future time-period. Theprogram code can cause the processing device to receive a plurality offiles defining abnormal data-point patterns to be identified in theprediction data, wherein each file in the plurality of files includescustomizable program-code for identifying a respective abnormal patternof data-point values in the prediction data. The program code can causethe processing device to automatically identify at least one abnormaldata-point pattern in the prediction data by interpreting and executingthe customizable program-code in the plurality of files. The programcode can cause the processing device to automatically determine anoverride process that corresponds to the at least one abnormaldata-point pattern in response to identifying the at least one abnormaldata-point pattern in the prediction data. The override process can beautomatically determined using correlations between the abnormaldata-point patterns and override processes. The override process caninvolve replacing a value of at least one data point in the predictiondata with another value that is configured to mitigate an impact of theat least one abnormal data-point pattern on the prediction. The programcode can cause the processing device to automatically generate acorrected version of the prediction data in response to determining theoverride process. The corrected version of the prediction data can begenerated by executing the override process. The program code can causethe processing device to automatically adjust one or more computerparameters based on the corrected version of the prediction data.

Yet another example of the present disclosure involves a method. Themethod can include receiving prediction data representing a prediction,wherein the prediction data forms a time series that spans a futuretime-period. The method can include receiving a plurality of filesdefining abnormal data-point patterns to be identified in the predictiondata, wherein each file in the plurality of files includes customizableprogram-code for identifying a respective abnormal pattern of data-pointvalues in the prediction data. The method can include automaticallyidentifying at least one abnormal data-point pattern in the predictiondata by interpreting and executing the customizable program-code in theplurality of files. The method can include automatically determining anoverride process that corresponds to the at least one abnormaldata-point pattern in response to identifying the at least one abnormaldata-point pattern in the prediction data. The override process can beautomatically determined using correlations between the abnormaldata-point patterns and override processes. The override process caninvolve replacing a value of at least one data point in the predictiondata with another value that is configured to mitigate an impact of theat least one abnormal data-point pattern on the prediction data. Themethod can include automatically generating a corrected version of theprediction data in response to determining the override process. Thecorrected version of the prediction data can be generated by executingthe override process. The method can include automatically adjusting oneor more computer parameters based on the corrected version of theprediction data. Some or all of these operations can be implemented by aprocessing device.

This summary is not intended to identify key or essential features ofthe claimed subject matter, nor is it intended to be used in isolationto determine the scope of the claimed subject matter. The subject mattershould be understood by reference to appropriate portions of the entirespecification, any or all drawings, and each claim.

The foregoing, together with other features and examples, will becomemore apparent upon referring to the following specification, claims, andaccompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawings will be provided by the office upon request and paymentof any necessary fee. The present disclosure is described in conjunctionwith the appended figures:

FIG. 1 is a block diagram of an example of the hardware components of acomputing system according to some aspects.

FIG. 2 is an example of devices that can communicate with each otherover an exchange system and via a network according to some aspects.

FIG. 3 is a block diagram of a model of an example of a communicationsprotocol system according to some aspects.

FIG. 4 is a hierarchical diagram of an example of a communications gridcomputing system including a variety of control and worker nodesaccording to some aspects.

FIG. 5 is a flow chart of an example of a process for adjusting acommunications grid or a work project in a communications grid after afailure of a node according to some aspects.

FIG. 6 is a block diagram of a portion of a communications gridcomputing system including a control node and a worker node according tosome aspects.

FIG. 7 is a flow chart of an example of a process for executing a dataanalysis or processing project according to some aspects.

FIG. 8 is a block diagram including components of an Event StreamProcessing Engine (ESPE) according to some aspects.

FIG. 9 is a flow chart of an example of a process including operationsperformed by an event stream processing engine according to someaspects.

FIG. 10 is a block diagram of an ESP system interfacing between apublishing device and multiple event subscribing devices according tosome aspects.

FIG. 11 is a flow chart of an example of a process for predicting andadjusting computer functionality according to some aspects.

FIG. 12 is an example of customizable program-code according to someaspects.

FIG. 13 is a graph of an example of a positive flat time-seriesaccording to some aspects.

FIG. 14 is a graph of an example of a short time series according tosome aspects.

FIG. 15 is a flow chart of an example of a process for identifying aretired or inactive time series according to some aspects.

FIG. 16 is a graph of an example of a time series with trailing zerosaccording to some aspects.

FIG. 17 is a graph of another example of a time series with trailingzeros according to some aspects.

FIG. 18 depicts graphs showing an example of a profile method foridentifying retired or inactive time series according to some aspects.

FIG. 19 depicts graphs showing another example of a profile method foridentifying retired or inactive time series according to some aspects.

In the appended figures, similar components or features can have thesame reference label. Further, various components of the same type canbe distinguished by following the reference label by a dash and a secondlabel that distinguishes among the similar components. If only the firstreference label is used in the specification, the description isapplicable to any one of the similar components having the same firstreference label irrespective of the second reference label.

DETAILED DESCRIPTION

Computers include various software and hardware components that areprone to failure. Such failures can temporarily or permanently affectthe computer's performance and functional capabilities. For example,computer failures can result in lost or corrupted data, a reducedability to process information, a reduced ability to store and retrievedata from memory, memory leaks, and other problems. It is thereforedesirable to avoid or reduce the impact of such failures in order toimprove the computer's overall performance.

Some examples of the present disclosure can overcome one or more of theabovementioned problems via a monitoring system associated with acomputer. The monitoring system can log various aspects of thecomputer's performance over a certain time period and then use the logto generate prediction data indicating how the computer will functionduring a future time-period. The prediction data is a time seriesrepresenting a prediction of at least one aspect of how the computerwill function during the future time-period. Since it can be challengingto accurately create the prediction data, the monitoring system cananalyze the prediction data for various abnormal data-point patternsindicating errors in the prediction data. An abnormal data-point patternis an abnormal pattern of data-point values. If an abnormal data-pointpattern is identified, the monitoring system can execute one or moreoverride processes to correct the prediction data. An override processis a process by which a value of a data point in the prediction data isreplaced with a new value in order to mitigate the effect of theabnormal data-point pattern on the prediction data. After correcting theprediction data, the monitoring system can analyze the correctedprediction data to identify one or more anomalies therein. An anomaly isa data-point value or a pattern of data-point values associated with acomputer failure. If the monitoring system identifies an anomaly, themonitoring system can adjust one or more parameters (e.g., hardware orsoftware parameters) of the computer. This may involve preemptivelyperforming maintenance, updates, or other operations to avoid or reducethe impact of the computer failure associated with the anomaly. Themonitoring system can perform some or all of these operationsautomatically (e.g., with little or no human involvement).

More specifically, the monitoring system can include a preprogrammed setof abnormal data-point patterns to search for in the prediction data.Examples of the abnormal data-point patterns can include (i) a positiveflat pattern in which all of the data-points in the prediction data havevalues that are equal and positive; (ii) an improper inactivity patternin which the prediction data has inactivity (e.g., data points withvalues of zero) during a time period in which a historical dataset thatwas used to create the prediction data had activity (e.g., data pointswith non-zero values); (iii) an extreme pattern in which the an averagevalue of the data points in the prediction data is at least a thresholdamount above or below a historical average during the same time periodin the historical dataset used to create the prediction data; or (iv)any combination of these. In other examples, the monitoring system canenable a user to define an abnormal data-point pattern using programcode or a graphical user interface. For example, the user can create afile with program code defining one or more abnormal data-point patternsin a format that is consumable by the monitoring system. The monitoringsystem can receive the file and execute the program code therein toidentify the abnormal data-point patterns in the prediction data.

In some examples, a community of users and developers can contributefiles defining abnormal data-point patterns to a repository. Themonitoring system can access (e.g., automatically or at the user'sdirection) the repository, download some or all of the files, andconfigure the files for consumption and execution by the monitoringsystem. In this manner, the monitoring system's capability to search forand correct abnormal data-point patterns in the prediction data can bemanually customized or automatically updated, for example, to helpensure that the prediction data is as accurate as possible.

Similarly to the above, in some examples the monitoring system caninclude a preprogrammed set of anomalies associated with computerfailures to search for in the prediction data. Alternatively, themonitoring system can enable a user to define an anomaly associated witha computer failure using program code or a graphical user interface. Forexample, the user can create a file with program code defining one ormore anomalies in a format that is consumable by the monitoring system.The monitoring system can receive the file and execute the program codetherein to identify the anomalies in the prediction data.

In some examples, a community of users and developers can contributefiles defining anomalies associated with computer failures to arepository. The monitoring system can access (e.g., automatically or atthe user's direction) the repository, download some or all of the files,and configure the files for consumption and execution by the monitoringsystem. In this manner, monitoring system's capability to search foranomalies and avoid computer failures associated with such anomalies canbe manually customized or automatically updated, for example, to avoidnewly identified computer-failures.

These illustrative examples are given to introduce the reader to thegeneral subject matter discussed here and are not intended to limit thescope of the disclosed concepts. The following sections describe variousadditional features and examples with reference to the drawings in whichlike numerals indicate like elements but, like the illustrativeexamples, should not be used to limit the present disclosure.

FIGS. 1-10 depict examples of systems and methods usable for predictingand adjusting computer functionality according to some aspects. Forexample, FIG. 1 is a block diagram of an example of the hardwarecomponents of a computing system according to some aspects. Datatransmission network 100 is a specialized computer system that may beused for processing large amounts of data where a large number ofcomputer processing cycles are required.

Data transmission network 100 may also include computing environment114. Computing environment 114 may be a specialized computer or othermachine that processes the data received within the data transmissionnetwork 100. The computing environment 114 may include one or more othersystems. For example, computing environment 114 may include a databasesystem 118 or a communications grid 120. The computing environment 114can include one or more processing devices (e.g., distributed over oneor more networks or otherwise in communication with one another) that,in some examples, can collectively be referred to as a processor or aprocessing device.

Data transmission network 100 also includes one or more network devices102. Network devices 102 may include client devices that can communicatewith computing environment 114. For example, network devices 102 maysend data to the computing environment 114 to be processed, may sendcommunications to the computing environment 114 to control differentaspects of the computing environment or the data it is processing, amongother reasons. Network devices 102 may interact with the computingenvironment 114 through a number of ways, such as, for example, over oneor more networks 108.

In some examples, network devices 102 may provide a large amount ofdata, either all at once or streaming over a period of time (e.g., usingevent stream processing (ESP)), to the computing environment 114 vianetworks 108. For example, the network devices 102 can transmitelectronic messages for predicting and adjusting computer functionality,all at once or streaming over a period of time, to the computingenvironment 114 via networks 108.

The network devices 102 may include network computers, sensors,databases, or other devices that may transmit or otherwise provide datato computing environment 114. For example, network devices 102 mayinclude local area network devices, such as routers, hubs, switches, orother computer networking devices. These devices may provide a varietyof stored or generated data, such as network data or data specific tothe network devices 102 themselves. Network devices 102 may also includesensors that monitor their environment or other devices to collect dataregarding that environment or those devices, and such network devices102 may provide data they collect over time. Network devices 102 mayalso include devices within the internet of things, such as deviceswithin a home automation network. Some of these devices may be referredto as edge devices, and may involve edge-computing circuitry. Data maybe transmitted by network devices 102 directly to computing environment114 or to network-attached data stores, such as network-attached datastores 110 for storage so that the data may be retrieved later by thecomputing environment 114 or other portions of data transmission network100. For example, the network devices 102 can transmit data usable forpredicting and adjusting computer functionality to a network-attacheddata store 110 for storage. The computing environment 114 may laterretrieve the data from the network-attached data store 110 and use thedata to predict and adjust computer functionality.

Network-attached data stores 110 can store data to be processed by thecomputing environment 114 as well as any intermediate or final datagenerated by the computing system in non-volatile memory. But in certainexamples, the configuration of the computing environment 114 allows itsoperations to be performed such that intermediate and final data resultscan be stored solely in volatile memory (e.g., RAM), without arequirement that intermediate or final data results be stored tonon-volatile types of memory (e.g., disk). This can be useful in certainsituations, such as when the computing environment 114 receives ad hocqueries from a user and when responses, which are generated byprocessing large amounts of data, need to be generated dynamically(e.g., on the fly). In this situation, the computing environment 114 maybe configured to retain the processed information within memory so thatresponses can be generated for the user at different levels of detail aswell as allow a user to interactively query against this information.

Network-attached data stores 110 may store a variety of different typesof data organized in a variety of different ways and from a variety ofdifferent sources. For example, network-attached data stores may includestorage other than primary storage located within computing environment114 that is directly accessible by processors located therein.Network-attached data stores may include secondary, tertiary orauxiliary storage, such as large hard drives, servers, virtual memory,among other types. Storage devices may include portable or non-portablestorage devices, optical storage devices, and various other mediumscapable of storing, containing data. A machine-readable storage mediumor computer-readable storage medium may include a non-transitory mediumin which data can be stored and that does not include carrier waves ortransitory electronic communications. Examples of a non-transitorymedium may include, for example, a magnetic disk or tape, opticalstorage media such as compact disk or digital versatile disk, flashmemory, memory or memory devices. A computer-program product may includecode or machine-executable instructions that may represent a procedure,a function, a subprogram, a program, a routine, a subroutine, a module,a software package, a class, or any combination of instructions, datastructures, or program statements. A code segment may be coupled toanother code segment or a hardware circuit by passing or receivinginformation, data, arguments, parameters, or memory contents.Information, arguments, parameters, data, etc. may be passed, forwarded,or transmitted via any suitable means including memory sharing, messagepassing, token passing, network transmission, among others. Furthermore,the data stores may hold a variety of different types of data. Forexample, network-attached data stores 110 may hold unstructured (e.g.,raw) data.

The unstructured data may be presented to the computing environment 114in different forms such as a flat file or a conglomerate of datarecords, and may have data values and accompanying time stamps. Thecomputing environment 114 may be used to analyze the unstructured datain a variety of ways to determine the best way to structure (e.g.,hierarchically) that data, such that the structured data is tailored toa type of further analysis that a user wishes to perform on the data.For example, after being processed, the unstructured time-stamped datamay be aggregated by time (e.g., into daily time period units) togenerate time series data or structured hierarchically according to oneor more dimensions (e.g., parameters, attributes, or variables). Forexample, data may be stored in a hierarchical data structure, such as arelational online analytical processing (ROLAP) or multidimensionalonline analytical processing (MOLAP) database, or may be stored inanother tabular form, such as in a flat-hierarchy form.

Data transmission network 100 may also include one or more server farms106. Computing environment 114 may route select communications or datato the sever farms 106 or one or more servers within the server farms106. Server farms 106 can be configured to provide information in apredetermined manner. For example, server farms 106 may access data totransmit in response to a communication. Server farms 106 may beseparately housed from each other device within data transmissionnetwork 100, such as computing environment 114, or may be part of adevice or system.

Server farms 106 may host a variety of different types of dataprocessing as part of data transmission network 100. Server farms 106may receive a variety of different data from network devices, fromcomputing environment 114, from cloud network 116, or from othersources. The data may have been obtained or collected from one or morewebsites, sensors, as inputs from a control database, or may have beenreceived as inputs from an external system or device. Server farms 106may assist in processing the data by turning raw data into processeddata based on one or more rules implemented by the server farms. Forexample, sensor data may be analyzed to determine changes in anenvironment over time or in real-time.

Data transmission network 100 may also include one or more cloudnetworks 116. Cloud network 116 may include a cloud infrastructuresystem that provides cloud services. In certain examples, servicesprovided by the cloud network 116 may include a host of services thatare made available to users of the cloud infrastructure system ondemand. Cloud network 116 is shown in FIG. 1 as being connected tocomputing environment 114 (and therefore having computing environment114 as its client or user), but cloud network 116 may be connected to orutilized by any of the devices in FIG. 1. Services provided by the cloudnetwork 116 can dynamically scale to meet the needs of its users. Thecloud network 116 may include one or more computers, servers, orsystems. In some examples, the computers, servers, or systems that makeup the cloud network 116 are different from the user's own on-premisescomputers, servers, or systems. For example, the cloud network 116 mayhost an application, and a user may, via a communication network such asthe Internet, order and use the application on demand. In some examples,the cloud network 116 may host an application for predicting andadjusting computer functionality.

While each device, server, and system in FIG. 1 is shown as a singledevice, multiple devices may instead be used. For example, a set ofnetwork devices can be used to transmit various communications from asingle user, or remote server 140 may include a server stack. As anotherexample, data may be processed as part of computing environment 114.

Each communication within data transmission network 100 (e.g., betweenclient devices, between a device and connection management system 150,between server farms 106 and computing environment 114, or between aserver and a device) may occur over one or more networks 108. Networks108 may include one or more of a variety of different types of networks,including a wireless network, a wired network, or a combination of awired and wireless network. Examples of suitable networks include theInternet, a personal area network, a local area network (LAN), a widearea network (WAN), or a wireless local area network (WLAN). A wirelessnetwork may include a wireless interface or combination of wirelessinterfaces. As an example, a network in the one or more networks 108 mayinclude a short-range communication channel, such as a Bluetooth or aBluetooth Low Energy channel. A wired network may include a wiredinterface. The wired or wireless networks may be implemented usingrouters, access points, bridges, gateways, or the like, to connectdevices in the network 108. The networks 108 can be incorporatedentirely within or can include an intranet, an extranet, or acombination thereof. In one example, communications between two or moresystems or devices can be achieved by a secure communications protocol,such as secure sockets layer (SSL) or transport layer security (TLS). Inaddition, data or transactional details may be encrypted.

Some aspects may utilize the Internet of Things (IoT), where things(e.g., machines, devices, phones, sensors) can be connected to networksand the data from these things can be collected and processed within thethings or external to the things. For example, the IoT can includesensors in many different devices, and high value analytics can beapplied to identify hidden relationships and drive increasedefficiencies. This can apply to both big data analytics and real-time(e.g., ESP) analytics.

As noted, computing environment 114 may include a communications grid120 and a transmission network database system 118. Communications grid120 may be a grid-based computing system for processing large amounts ofdata. The transmission network database system 118 may be for managing,storing, and retrieving large amounts of data that are distributed toand stored in the one or more network-attached data stores 110 or otherdata stores that reside at different locations within the transmissionnetwork database system 118. The computing nodes in the communicationsgrid 120 and the transmission network database system 118 may share thesame processor hardware, such as processors that are located withincomputing environment 114.

In some examples, the computing environment 114, a network device 102,or both can implement one or more processes for predicting and adjustingcomputer functionality. For example, the computing environment 114, anetwork device 102, or both can implement one or more versions of theprocesses discussed with respect to any of the figures.

FIG. 2 is an example of devices that can communicate with each otherover an exchange system and via a network according to some aspects. Asnoted, each communication within data transmission network 100 may occurover one or more networks. System 200 includes a network device 204configured to communicate with a variety of types of client devices, forexample client devices 230, over a variety of types of communicationchannels.

As shown in FIG. 2, network device 204 can transmit a communication overa network (e.g., a cellular network via a base station 210). In someexamples, the communication can include times series data. Thecommunication can be routed to another network device, such as networkdevices 205-209, via base station 210. The communication can also berouted to computing environment 214 via base station 210. In someexamples, the network device 204 may collect data either from itssurrounding environment or from other network devices (such as networkdevices 205-209) and transmit that data to computing environment 214.

Although network devices 204-209 are shown in FIG. 2 as a mobile phone,laptop computer, tablet computer, temperature sensor, motion sensor, andaudio sensor respectively, the network devices may be or include sensorsthat are sensitive to detecting aspects of their environment. Forexample, the network devices may include sensors such as water sensors,power sensors, electrical current sensors, chemical sensors, opticalsensors, pressure sensors, geographic or position sensors (e.g., GPS),velocity sensors, acceleration sensors, flow rate sensors, among others.Examples of characteristics that may be sensed include force, torque,load, strain, position, temperature, air pressure, fluid flow, chemicalproperties, resistance, electromagnetic fields, radiation, irradiance,proximity, acoustics, moisture, distance, speed, vibrations,acceleration, electrical potential, and electrical current, amongothers. The sensors may be mounted to various components used as part ofa variety of different types of systems. The network devices may detectand record data related to the environment that it monitors, andtransmit that data to computing environment 214.

The network devices 204-209 may also perform processing on data itcollects before transmitting the data to the computing environment 214,or before deciding whether to transmit data to the computing environment214. For example, network devices 204-209 may determine whether datacollected meets certain rules, for example by comparing data or valuescalculated from the data and comparing that data to one or morethresholds. The network devices 204-209 may use this data or comparisonsto determine if the data is to be transmitted to the computingenvironment 214 for further use or processing. In some examples, thenetwork devices 204-209 can pre-process the data prior to transmittingthe data to the computing environment 214. For example, the networkdevices 204-209 can reformat the data before transmitting the data tothe computing environment 214 for further processing (e.g., analyzingthe data to predict and adjust computer functionality).

Computing environment 214 may include machines 220, 240. Althoughcomputing environment 214 is shown in FIG. 2 as having two machines 220,240, computing environment 214 may have only one machine or may havemore than two machines. The machines 220, 240 that make up computingenvironment 214 may include specialized computers, servers, or othermachines that are configured to individually or collectively processlarge amounts of data. The computing environment 214 may also includestorage devices that include one or more databases of structured data,such as data organized in one or more hierarchies, or unstructured data.The databases may communicate with the processing devices withincomputing environment 214 to distribute data to them. Since networkdevices may transmit data to computing environment 214, that data may bereceived by the computing environment 214 and subsequently stored withinthose storage devices. Data used by computing environment 214 may alsobe stored in data stores 235, which may also be a part of or connectedto computing environment 214.

Computing environment 214 can communicate with various devices via oneor more routers 225 or other inter-network or intra-network connectioncomponents. For example, computing environment 214 may communicate withclient devices 230 via one or more routers 225. Computing environment214 may collect, analyze or store data from or pertaining tocommunications, client device operations, client rules, oruser-associated actions stored at one or more data stores 235. Such datamay influence communication routing to the devices within computingenvironment 214, how data is stored or processed within computingenvironment 214, among other actions.

Notably, various other devices can further be used to influencecommunication routing or processing between devices within computingenvironment 214 and with devices outside of computing environment 214.For example, as shown in FIG. 2, computing environment 214 may include amachine 240 that is a web server. Computing environment 214 can retrievedata of interest, such as client information (e.g., product information,client rules, etc.), technical product details, news, blog posts,e-mails, forum posts, electronic documents, social media posts (e.g.,Twitter™ posts or Facebook™ posts), time series data, and so on.

In addition to computing environment 214 collecting data (e.g., asreceived from network devices, such as sensors, and client devices orother sources) to be processed as part of a big data analytics project,it may also receive data in real time as part of a streaming analyticsenvironment. As noted, data may be collected using a variety of sourcesas communicated via different kinds of networks or locally. Such datamay be received on a real-time streaming basis. For example, networkdevices 204-209 may receive data periodically and in real time from aweb server or other source. Devices within computing environment 214 mayalso perform pre-analysis on data it receives to determine if the datareceived should be processed as part of an ongoing project. For example,as part of a project in which computer functionality is predicted andadjusted via data, the computing environment 214 can perform apre-analysis of the data. The pre-analysis can include determiningwhether the data is in a correct format for predicting and adjustingcomputer functionality using the data and, if not, reformatting the datainto the correct format.

FIG. 3 is a block diagram of a model of an example of a communicationsprotocol system according to some aspects. More specifically, FIG. 3identifies operation of a computing environment in an Open SystemsInteraction model that corresponds to various connection components. Themodel 300 shows, for example, how a computing environment, such ascomputing environment (or computing environment 214 in FIG. 2) maycommunicate with other devices in its network, and control howcommunications between the computing environment and other devices areexecuted and under what conditions.

The model 300 can include layers 302-314. The layers 302-314 arearranged in a stack. Each layer in the stack serves the layer one levelhigher than it (except for the application layer, which is the highestlayer), and is served by the layer one level below it (except for thephysical layer 302, which is the lowest layer). The physical layer 302is the lowest layer because it receives and transmits raw bites of data,and is the farthest layer from the user in a communications system. Onthe other hand, the application layer is the highest layer because itinteracts directly with a software application.

As noted, the model 300 includes a physical layer 302. Physical layer302 represents physical communication, and can define parameters of thatphysical communication. For example, such physical communication maycome in the form of electrical, optical, or electromagneticcommunications. Physical layer 302 also defines protocols that maycontrol communications within a data transmission network.

Link layer 304 defines links and mechanisms used to transmit (e.g.,move) data across a network. The link layer manages node-to-nodecommunications, such as within a grid-computing environment. Link layer304 can detect and correct errors (e.g., transmission errors in thephysical layer 302). Link layer 304 can also include a media accesscontrol (MAC) layer and logical link control (LLC) layer.

Network layer 306 can define the protocol for routing within a network.In other words, the network layer coordinates transferring data acrossnodes in a same network (e.g., such as a grid-computing environment).Network layer 306 can also define the processes used to structure localaddressing within the network.

Transport layer 308 can manage the transmission of data and the qualityof the transmission or receipt of that data. Transport layer 308 canprovide a protocol for transferring data, such as, for example, aTransmission Control Protocol (TCP). Transport layer 308 can assembleand disassemble data frames for transmission. The transport layer canalso detect transmission errors occurring in the layers below it.

Session layer 310 can establish, maintain, and manage communicationconnections between devices on a network. In other words, the sessionlayer controls the dialogues or nature of communications between networkdevices on the network. The session layer may also establishcheckpointing, adjournment, termination, and restart procedures.

Presentation layer 312 can provide translation for communicationsbetween the application and network layers. In other words, this layermay encrypt, decrypt or format data based on data types known to beaccepted by an application or network layer.

Application layer 314 interacts directly with software applications andend users, and manages communications between them. Application layer314 can identify destinations, local resource states or availability orcommunication content or formatting using the applications.

For example, a communication link can be established between two deviceson a network. One device can transmit an analog or digitalrepresentation of an electronic message that includes a data set to theother device. The other device can receive the analog or digitalrepresentation at the physical layer 302. The other device can transmitthe data associated with the electronic message through the remaininglayers 304-314. The application layer 314 can receive data associatedwith the electronic message. The application layer 314 can identify oneor more applications, such as an application for predicting andadjusting computer functionality, to which to transmit data associatedwith the electronic message. The application layer 314 can transmit thedata to the identified application.

Intra-network connection components 322, 324 can operate in lowerlevels, such as physical layer 302 and link layer 304, respectively. Forexample, a hub can operate in the physical layer, a switch can operatein the physical layer, and a router can operate in the network layer.Inter-network connection components 326, 328 are shown to operate onhigher levels, such as layers 306-314. For example, routers can operatein the network layer and network devices can operate in the transport,session, presentation, and application layers.

A computing environment 330 can interact with or operate on, in variousexamples, one, more, all or any of the various layers. For example,computing environment 330 can interact with a hub (e.g., via the linklayer) to adjust which devices the hub communicates with. The physicallayer 302 may be served by the link layer 304, so it may implement suchdata from the link layer 304. For example, the computing environment 330may control which devices from which it can receive data. For example,if the computing environment 330 knows that a certain network device hasturned off, broken, or otherwise become unavailable or unreliable, thecomputing environment 330 may instruct the hub to prevent any data frombeing transmitted to the computing environment 330 from that networkdevice. Such a process may be beneficial to avoid receiving data that isinaccurate or that has been influenced by an uncontrolled environment.As another example, computing environment 330 can communicate with abridge, switch, router or gateway and influence which device within thesystem (e.g., system 200) the component selects as a destination. Insome examples, computing environment 330 can interact with variouslayers by exchanging communications with equipment operating on aparticular layer by routing or modifying existing communications. Inanother example, such as in a grid-computing environment, a node maydetermine how data within the environment should be routed (e.g., whichnode should receive certain data) based on certain parameters orinformation provided by other layers within the model.

The computing environment 330 may be a part of a communications gridenvironment, the communications of which may be implemented as shown inthe protocol of FIG. 3. For example, referring back to FIG. 2, one ormore of machines 220 and 240 may be part of a communicationsgrid-computing environment. A gridded computing environment may beemployed in a distributed system with non-interactive workloads wheredata resides in memory on the machines, or compute nodes. In such anenvironment, analytic code, instead of a database management system, cancontrol the processing performed by the nodes. Data is co-located bypre-distributing it to the grid nodes, and the analytic code on eachnode loads the local data into memory. Each node may be assigned aparticular task, such as a portion of a processing project, or toorganize or control other nodes within the grid. For example, each nodemay be assigned a portion of a processing task for predicting andadjusting computer functionality.

FIG. 4 is a hierarchical diagram of an example of a communications gridcomputing system 400 including a variety of control and worker nodesaccording to some aspects. Communications grid computing system 400includes three control nodes and one or more worker nodes.Communications grid computing system 400 includes control nodes 402,404, and 406. The control nodes are communicatively connected viacommunication paths 451, 453, and 455. The control nodes 402-406 maytransmit information (e.g., related to the communications grid ornotifications) to and receive information from each other. Althoughcommunications grid computing system 400 is shown in FIG. 4 as includingthree control nodes, the communications grid may include more or lessthan three control nodes.

Communications grid computing system 400 (which can be referred to as a“communications grid”) also includes one or more worker nodes. Shown inFIG. 4 are six worker nodes 410-420. Although FIG. 4 shows six workernodes, a communications grid can include more or less than six workernodes. The number of worker nodes included in a communications grid maybe dependent upon how large the project or data set is being processedby the communications grid, the capacity of each worker node, the timedesignated for the communications grid to complete the project, amongothers. Each worker node within the communications grid computing system400 may be connected (wired or wirelessly, and directly or indirectly)to control nodes 402-406. Each worker node may receive information fromthe control nodes (e.g., an instruction to perform work on a project)and may transmit information to the control nodes (e.g., a result fromwork performed on a project). Furthermore, worker nodes may communicatewith each other directly or indirectly. For example, worker nodes maytransmit data between each other related to a job being performed or anindividual task within a job being performed by that worker node. Insome examples, worker nodes may not be connected (communicatively orotherwise) to certain other worker nodes. For example, a worker node 410may only be able to communicate with a particular control node 402. Theworker node 410 may be unable to communicate with other worker nodes412-420 in the communications grid, even if the other worker nodes412-420 are controlled by the same control node 402.

A control node 402-406 may connect with an external device with whichthe control node 402-406 may communicate (e.g., a communications griduser, such as a server or computer, may connect to a controller of thegrid). For example, a server or computer may connect to control nodes402-406 and may transmit a project or job to the node, such as a projector job related to predicting and adjusting computer functionality. Theproject may include the data set. The data set may be of any size andcan include a time series. Once the control node 402-406 receives such aproject including a large data set, the control node may distribute thedata set or projects related to the data set to be performed by workernodes. Alternatively, for a project including a large data set, the dataset may be receive or stored by a machine other than a control node402-406 (e.g., a Hadoop data node).

Control nodes 402-406 can maintain knowledge of the status of the nodesin the grid (e.g., grid status information), accept work requests fromclients, subdivide the work across worker nodes, and coordinate theworker nodes, among other responsibilities. Worker nodes 412-420 mayaccept work requests from a control node 402-406 and provide the controlnode with results of the work performed by the worker node. A grid maybe started from a single node (e.g., a machine, computer, server, etc.).This first node may be assigned or may start as the primary control node402 that will control any additional nodes that enter the grid.

When a project is submitted for execution (e.g., by a client or acontroller of the grid) it may be assigned to a set of nodes. After thenodes are assigned to a project, a data structure (e.g., a communicator)may be created. The communicator may be used by the project forinformation to be shared between the project code running on each node.A communication handle may be created on each node. A handle, forexample, is a reference to the communicator that is valid within asingle process on a single node, and the handle may be used whenrequesting communications between nodes.

A control node, such as control node 402, may be designated as theprimary control node. A server, computer or other external device mayconnect to the primary control node. Once the control node 402 receivesa project, the primary control node may distribute portions of theproject to its worker nodes for execution. For example, a project forpredicting and adjusting computer functionality can be initiated oncommunications grid computing system 400. A primary control node cancontrol the work to be performed for the project in order to completethe project as requested or instructed. The primary control node maydistribute work to the worker nodes 412-420 based on various factors,such as which subsets or portions of projects may be completed mostefficiently and in the correct amount of time. For example, a workernode 412 may predict and adjust computer functionality using at least aportion of data that is already local (e.g., stored on) the worker node.The primary control node also coordinates and processes the results ofthe work performed by each worker node 412-420 after each worker node412-420 executes and completes its job. For example, the primary controlnode may receive a result from one or more worker nodes 412-420, and theprimary control node may organize (e.g., collect and assemble) theresults received and compile them to produce a complete result for theproject received from the end user.

Any remaining control nodes, such as control nodes 404, 406, may beassigned as backup control nodes for the project. In an example, backupcontrol nodes may not control any portion of the project. Instead,backup control nodes may serve as a backup for the primary control nodeand take over as primary control node if the primary control node wereto fail. If a communications grid were to include only a single controlnode 402, and the control node 402 were to fail (e.g., the control nodeis shut off or breaks) then the communications grid as a whole may failand any project or job being run on the communications grid may fail andmay not complete. While the project may be run again, such a failure maycause a delay (severe delay in some cases, such as overnight delay) incompletion of the project. Therefore, a grid with multiple control nodes402-406, including a backup control node, may be beneficial.

In some examples, the primary control node may open a pair of listeningsockets to add another node or machine to the grid. A socket may be usedto accept work requests from clients, and the second socket may be usedto accept connections from other grid nodes. The primary control nodemay be provided with a list of other nodes (e.g., other machines,computers, servers, etc.) that can participate in the grid, and the rolethat each node can fill in the grid. Upon startup of the primary controlnode (e.g., the first node on the grid), the primary control node mayuse a network protocol to start the server process on every other nodein the grid. Command line parameters, for example, may inform each nodeof one or more pieces of information, such as: the role that the nodewill have in the grid, the host name of the primary control node, theport number on which the primary control node is accepting connectionsfrom peer nodes, among others. The information may also be provided in aconfiguration file, transmitted over a secure shell tunnel, recoveredfrom a configuration server, among others. While the other machines inthe grid may not initially know about the configuration of the grid,that information may also be sent to each other node by the primarycontrol node. Updates of the grid information may also be subsequentlysent to those nodes.

For any control node other than the primary control node added to thegrid, the control node may open three sockets. The first socket mayaccept work requests from clients, the second socket may acceptconnections from other grid members, and the third socket may connect(e.g., permanently) to the primary control node. When a control node(e.g., primary control node) receives a connection from another controlnode, it first checks to see if the peer node is in the list ofconfigured nodes in the grid. If it is not on the list, the control nodemay clear the connection. If it is on the list, it may then attempt toauthenticate the connection. If authentication is successful, theauthenticating node may transmit information to its peer, such as theport number on which a node is listening for connections, the host nameof the node, information about how to authenticate the node, among otherinformation. When a node, such as the new control node, receivesinformation about another active node, it can check to see if it alreadyhas a connection to that other node. If it does not have a connection tothat node, it may then establish a connection to that control node.

Any worker node added to the grid may establish a connection to theprimary control node and any other control nodes on the grid. Afterestablishing the connection, it may authenticate itself to the grid(e.g., any control nodes, including both primary and backup, or a serveror user controlling the grid). After successful authentication, theworker node may accept configuration information from the control node.

When a node joins a communications grid (e.g., when the node is poweredon or connected to an existing node on the grid or both), the node isassigned (e.g., by an operating system of the grid) a universally uniqueidentifier (UUID). This unique identifier may help other nodes andexternal entities (devices, users, etc.) to identify the node anddistinguish it from other nodes. When a node is connected to the grid,the node may share its unique identifier with the other nodes in thegrid. Since each node may share its unique identifier, each node mayknow the unique identifier of every other node on the grid. Uniqueidentifiers may also designate a hierarchy of each of the nodes (e.g.,backup control nodes) within the grid. For example, the uniqueidentifiers of each of the backup control nodes may be stored in a listof backup control nodes to indicate an order in which the backup controlnodes will take over for a failed primary control node to become a newprimary control node. But, a hierarchy of nodes may also be determinedusing methods other than using the unique identifiers of the nodes. Forexample, the hierarchy may be predetermined, or may be assigned based onother predetermined factors.

The grid may add new machines at any time (e.g., initiated from anycontrol node). Upon adding a new node to the grid, the control node mayfirst add the new node to its table of grid nodes. The control node mayalso then notify every other control node about the new node. The nodesreceiving the notification may acknowledge that they have updated theirconfiguration information.

Primary control node 402 may, for example, transmit one or morecommunications to backup control nodes 404, 406 (and, for example, toother control or worker nodes 412-420 within the communications grid).Such communications may be sent periodically, at fixed time intervals,between known fixed stages of the project's execution, among otherprotocols. The communications transmitted by primary control node 402may be of varied types and may include a variety of types ofinformation. For example, primary control node 402 may transmitsnapshots (e.g., status information) of the communications grid so thatbackup control node 404 always has a recent snapshot of thecommunications grid. The snapshot or grid status may include, forexample, the structure of the grid (including, for example, the workernodes 410-420 in the communications grid, unique identifiers of theworker nodes 410-420, or their relationships with the primary controlnode 402) and the status of a project (including, for example, thestatus of each worker node's portion of the project). The snapshot mayalso include analysis or results received from worker nodes 410-420 inthe communications grid. The backup control nodes 404, 406 may receiveand store the backup data received from the primary control node 402.The backup control nodes 404, 406 may transmit a request for such asnapshot (or other information) from the primary control node 402, orthe primary control node 402 may send such information periodically tothe backup control nodes 404, 406.

As noted, the backup data may allow a backup control node 404, 406 totake over as primary control node if the primary control node 402 failswithout requiring the communications grid to start the project over fromscratch. If the primary control node 402 fails, the backup control node404, 406 that will take over as primary control node may retrieve themost recent version of the snapshot received from the primary controlnode 402 and use the snapshot to continue the project from the stage ofthe project indicated by the backup data. This may prevent failure ofthe project as a whole.

A backup control node 404, 406 may use various methods to determine thatthe primary control node 402 has failed. In one example of such amethod, the primary control node 402 may transmit (e.g., periodically) acommunication to the backup control node 404, 406 that indicates thatthe primary control node 402 is working and has not failed, such as aheartbeat communication. The backup control node 404, 406 may determinethat the primary control node 402 has failed if the backup control nodehas not received a heartbeat communication for a certain predeterminedperiod of time. Alternatively, a backup control node 404, 406 may alsoreceive a communication from the primary control node 402 itself (beforeit failed) or from a worker node 410-420 that the primary control node402 has failed, for example because the primary control node 402 hasfailed to communicate with the worker node 410-420.

Different methods may be performed to determine which backup controlnode of a set of backup control nodes (e.g., backup control nodes 404,406) can take over for failed primary control node 402 and become thenew primary control node. For example, the new primary control node maybe chosen based on a ranking or “hierarchy” of backup control nodesbased on their unique identifiers. In an alternative example, a backupcontrol node may be assigned to be the new primary control node byanother device in the communications grid or from an external device(e.g., a system infrastructure or an end user, such as a server orcomputer, controlling the communications grid). In another alternativeexample, the backup control node that takes over as the new primarycontrol node may be designated based on bandwidth or other statisticsabout the communications grid.

A worker node within the communications grid may also fail. If a workernode fails, work being performed by the failed worker node may beredistributed amongst the operational worker nodes. In an alternativeexample, the primary control node may transmit a communication to eachof the operable worker nodes still on the communications grid that eachof the worker nodes should purposefully fail also. After each of theworker nodes fail, they may each retrieve their most recent savedcheckpoint of their status and re-start the project from that checkpointto minimize lost progress on the project being executed. In someexamples, a communications grid computing system 400 can be used topredict and adjust computer functionality.

FIG. 5 is a flow chart of an example of a process for adjusting acommunications grid or a work project in a communications grid after afailure of a node according to some aspects. The process may include,for example, receiving grid status information including a projectstatus of a portion of a project being executed by a node in thecommunications grid, as described in operation 502. For example, acontrol node (e.g., a backup control node connected to a primary controlnode and a worker node on a communications grid) may receive grid statusinformation, where the grid status information includes a project statusof the primary control node or a project status of the worker node. Theproject status of the primary control node and the project status of theworker node may include a status of one or more portions of a projectbeing executed by the primary and worker nodes in the communicationsgrid. The process may also include storing the grid status information,as described in operation 504. For example, a control node (e.g., abackup control node) may store the received grid status informationlocally within the control node. Alternatively, the grid statusinformation may be sent to another device for storage where the controlnode may have access to the information.

The process may also include receiving a failure communicationcorresponding to a node in the communications grid in operation 506. Forexample, a node may receive a failure communication including anindication that the primary control node has failed, prompting a backupcontrol node to take over for the primary control node. In analternative embodiment, a node may receive a failure that a worker nodehas failed, prompting a control node to reassign the work beingperformed by the worker node. The process may also include reassigning anode or a portion of the project being executed by the failed node, asdescribed in operation 508. For example, a control node may designatethe backup control node as a new primary control node based on thefailure communication upon receiving the failure communication. If thefailed node is a worker node, a control node may identify a projectstatus of the failed worker node using the snapshot of thecommunications grid, where the project status of the failed worker nodeincludes a status of a portion of the project being executed by thefailed worker node at the failure time.

The process may also include receiving updated grid status informationbased on the reassignment, as described in operation 510, andtransmitting a set of instructions based on the updated grid statusinformation to one or more nodes in the communications grid, asdescribed in operation 512. The updated grid status information mayinclude an updated project status of the primary control node or anupdated project status of the worker node. The updated information maybe transmitted to the other nodes in the grid to update their stalestored information.

FIG. 6 is a block diagram of a portion of a communications gridcomputing system 600 including a control node and a worker nodeaccording to some aspects. Communications grid 600 computing systemincludes one control node (control node 602) and one worker node (workernode 610) for purposes of illustration, but may include more workerand/or control nodes. The control node 602 is communicatively connectedto worker node 610 via communication path 650. Therefore, control node602 may transmit information (e.g., related to the communications gridor notifications), to and receive information from worker node 610 viacommunication path 650.

Similar to in FIG. 4, communications grid computing system (or just“communications grid”) 600 includes data processing nodes (control node602 and worker node 610). Nodes 602 and 610 comprise multi-core dataprocessors. Each node 602 and 610 includes a grid-enabled softwarecomponent (GESC) 620 that executes on the data processor associated withthat node and interfaces with buffer memory 622 also associated withthat node. Each node 602 and 610 includes database management software(DBMS) 628 that executes on a database server (not shown) at controlnode 602 and on a database server (not shown) at worker node 610.

Each node also includes a data store 624. Data stores 624, similar tonetwork-attached data stores 110 in FIG. 1 and data stores 235 in FIG.2, are used to store data to be processed by the nodes in the computingenvironment. Data stores 624 may also store any intermediate or finaldata generated by the computing system after being processed, forexample in non-volatile memory. However in certain examples, theconfiguration of the grid computing environment allows its operations tobe performed such that intermediate and final data results can be storedsolely in volatile memory (e.g., RAM), without a requirement thatintermediate or final data results be stored to non-volatile types ofmemory. Storing such data in volatile memory may be useful in certainsituations, such as when the grid receives queries (e.g., ad hoc) from aclient and when responses, which are generated by processing largeamounts of data, need to be generated quickly or on-the-fly. In such asituation, the grid may be configured to retain the data within memoryso that responses can be generated at different levels of detail and sothat a client may interactively query against this information.

Each node also includes a user-defined function (UDF) 626. The UDFprovides a mechanism for the DMBS 628 to transfer data to or receivedata from the database stored in the data stores 624 that are managed bythe DBMS. For example, UDF 626 can be invoked by the DBMS to providedata to the GESC for processing. The UDF 626 may establish a socketconnection (not shown) with the GESC to transfer the data.Alternatively, the UDF 626 can transfer data to the GESC by writing datato shared memory accessible by both the UDF and the GESC.

The GESC 620 at the nodes 602 and 610 may be connected via a network,such as network 108 shown in FIG. 1. Therefore, nodes 602 and 610 cancommunicate with each other via the network using a predeterminedcommunication protocol such as, for example, the Message PassingInterface (MPI). Each GESC 620 can engage in point-to-pointcommunication with the GESC at another node or in collectivecommunication with multiple GESCs via the network. The GESC 620 at eachnode may contain identical (or nearly identical) software instructions.Each node may be capable of operating as either a control node or aworker node. The GESC at the control node 602 can communicate, over acommunication path 652, with a client device 630. More specifically,control node 602 may communicate with client application 632 hosted bythe client device 630 to receive queries and to respond to those queriesafter processing large amounts of data.

DMBS 628 may control the creation, maintenance, and use of database ordata structure (not shown) within nodes 602 or 610. The database mayorganize data stored in data stores 624. The DMBS 628 at control node602 may accept requests for data and transfer the appropriate data forthe request. With such a process, collections of data may be distributedacross multiple physical locations. In this example, each node 602 and610 stores a portion of the total data managed by the management systemin its associated data store 624.

Furthermore, the DBMS may be responsible for protecting against dataloss using replication techniques. Replication includes providing abackup copy of data stored on one node on one or more other nodes.Therefore, if one node fails, the data from the failed node can berecovered from a replicated copy residing at another node. However, asdescribed herein with respect to FIG. 4, data or status information foreach node in the communications grid may also be shared with each nodeon the grid.

FIG. 7 is a flow chart of an example of a process for executing a dataanalysis or a processing project according to some aspects. As describedwith respect to FIG. 6, the GESC at the control node may transmit datawith a client device (e.g., client device 630) to receive queries forexecuting a project and to respond to those queries after large amountsof data have been processed. The query may be transmitted to the controlnode, where the query may include a request for executing a project, asdescribed in operation 702. The query can contain instructions on thetype of data analysis to be performed in the project and whether theproject should be executed using the grid-based computing environment,as shown in operation 704.

To initiate the project, the control node may determine if the queryrequests use of the grid-based computing environment to execute theproject. If the determination is no, then the control node initiatesexecution of the project in a solo environment (e.g., at the controlnode), as described in operation 710. If the determination is yes, thecontrol node may initiate execution of the project in the grid-basedcomputing environment, as described in operation 706. In such asituation, the request may include a requested configuration of thegrid. For example, the request may include a number of control nodes anda number of worker nodes to be used in the grid when executing theproject. After the project has been completed, the control node maytransmit results of the analysis yielded by the grid, as described inoperation 708. Whether the project is executed in a solo or grid-basedenvironment, the control node provides the results of the project.

As noted with respect to FIG. 2, the computing environments describedherein may collect data (e.g., as received from network devices, such assensors, such as network devices 204-209 in FIG. 2, and client devicesor other sources) to be processed as part of a data analytics project,and data may be received in real time as part of a streaming analyticsenvironment (e.g., ESP). Data may be collected using a variety ofsources as communicated via different kinds of networks or locally, suchas on a real-time streaming basis. For example, network devices mayreceive data periodically from network device sensors as the sensorscontinuously sense, monitor and track changes in their environments.More specifically, an increasing number of distributed applicationsdevelop or produce continuously flowing data from distributed sources byapplying queries to the data before distributing the data togeographically distributed recipients. An event stream processing engine(ESPE) may continuously apply the queries to the data as it is receivedand determines which entities should receive the data. Client or otherdevices may also subscribe to the ESPE or other devices processing ESPdata so that they can receive data after processing, based on forexample the entities determined by the processing engine. For example,client devices 230 in FIG. 2 may subscribe to the ESPE in computingenvironment 214. In another example, event subscription devices 1024a-c, described further with respect to FIG. 10, may also subscribe tothe ESPE. The ESPE may determine or define how input data or eventstreams from network devices or other publishers (e.g., network devices204-209 in FIG. 2) are transformed into meaningful output data to beconsumed by subscribers, such as for example client devices 230 in FIG.2.

FIG. 8 is a block diagram including components of an Event StreamProcessing Engine (ESPE) according to some aspects. ESPE 800 may includeone or more projects 802. A project may be described as a second-levelcontainer in an engine model managed by ESPE 800 where a thread poolsize for the project may be defined by a user. Each project of the oneor more projects 802 may include one or more continuous queries 804 thatcontain data flows, which are data transformations of incoming eventstreams. The one or more continuous queries 804 may include one or moresource windows 806 and one or more derived windows 808.

The ESPE may receive streaming data over a period of time related tocertain events, such as events or other data sensed by one or morenetwork devices. The ESPE may perform operations associated withprocessing data created by the one or more devices. For example, theESPE may receive data from the one or more network devices 204-209 shownin FIG. 2. As noted, the network devices may include sensors that sensedifferent aspects of their environments, and may collect data over timebased on those sensed observations. For example, the ESPE may beimplemented within one or more of machines 220 and 240 shown in FIG. 2.The ESPE may be implemented within such a machine by an ESP application.An ESP application may embed an ESPE with its own dedicated thread poolor pools into its application space where the main application threadcan do application-specific work and the ESPE processes event streams atleast by creating an instance of a model into processing objects.

The engine container is the top-level container in a model that managesthe resources of the one or more projects 802. In an illustrativeexample, there may be only one ESPE 800 for each instance of the ESPapplication, and ESPE 800 may have a unique engine name. Additionally,the one or more projects 802 may each have unique project names, andeach query may have a unique continuous query name and begin with auniquely named source window of the one or more source windows 806. ESPE800 may or may not be persistent.

Continuous query modeling involves defining directed graphs of windowsfor event stream manipulation and transformation. A window in thecontext of event stream manipulation and transformation is a processingnode in an event stream processing model. A window in a continuous querycan perform aggregations, computations, pattern-matching, and otheroperations on data flowing through the window. A continuous query may bedescribed as a directed graph of source, relational, pattern matching,and procedural windows. The one or more source windows 806 and the oneor more derived windows 808 represent continuously executing queriesthat generate updates to a query result set as new event blocks streamthrough ESPE 800. A directed graph, for example, is a set of nodesconnected by edges, where the edges have a direction associated withthem.

An event object may be described as a packet of data accessible as acollection of fields, with at least one of the fields defined as a keyor unique identifier (ID). The event object may be created using avariety of formats including binary, alphanumeric, XML, etc. Each eventobject may include one or more fields designated as a primary identifier(ID) for the event so ESPE 800 can support operation codes (opcodes) forevents including insert, update, upsert, and delete. Upsert opcodesupdate the event if the key field already exists; otherwise, the eventis inserted. For illustration, an event object may be a packed binaryrepresentation of a set of field values and include both metadata andfield data associated with an event. The metadata may include an opcodeindicating if the event represents an insert, update, delete, or upsert,a set of flags indicating if the event is a normal, partial-update, or aretention generated event from retention policy management, and a set ofmicrosecond timestamps that can be used for latency measurements.

An event block object may be described as a grouping or package of eventobjects. An event stream may be described as a flow of event blockobjects. A continuous query of the one or more continuous queries 804transforms a source event stream made up of streaming event blockobjects published into ESPE 800 into one or more output event streamsusing the one or more source windows 806 and the one or more derivedwindows 808. A continuous query can also be thought of as data flowmodeling.

The one or more source windows 806 are at the top of the directed graphand have no windows feeding into them. Event streams are published intothe one or more source windows 806, and from there, the event streamsmay be directed to the next set of connected windows as defined by thedirected graph. The one or more derived windows 808 are all instantiatedwindows that are not source windows and that have other windowsstreaming events into them. The one or more derived windows 808 mayperform computations or transformations on the incoming event streams.The one or more derived windows 808 transform event streams based on thewindow type (that is operators such as join, filter, compute, aggregate,copy, pattern match, procedural, union, etc.) and window settings. Asevent streams are published into ESPE 800, they are continuouslyqueried, and the resulting sets of derived windows in these queries arecontinuously updated.

FIG. 9 is a flow chart of an example of a process including operationsperformed by an event stream processing engine according to someaspects. As noted, the ESPE 800 (or an associated ESP application)defines how input event streams are transformed into meaningful outputevent streams. More specifically, the ESP application may define howinput event streams from publishers (e.g., network devices providingsensed data) are transformed into meaningful output event streamsconsumed by subscribers (e.g., a data analytics project being executedby a machine or set of machines).

Within the application, a user may interact with one or more userinterface windows presented to the user in a display under control ofthe ESPE independently or through a browser application in an orderselectable by the user. For example, a user may execute an ESPapplication, which causes presentation of a first user interface window,which may include a plurality of menus and selectors such as drop downmenus, buttons, text boxes, hyperlinks, etc. associated with the ESPapplication as understood by a person of skill in the art. Variousoperations may be performed in parallel, for example, using a pluralityof threads.

At operation 900, an ESP application may define and start an ESPE,thereby instantiating an ESPE at a device, such as machine 220 and/or240. In an operation 902, the engine container is created. Forillustration, ESPE 800 may be instantiated using a function call thatspecifies the engine container as a manager for the model.

In an operation 904, the one or more continuous queries 804 areinstantiated by ESPE 800 as a model. The one or more continuous queries804 may be instantiated with a dedicated thread pool or pools thatgenerate updates as new events stream through ESPE 800. Forillustration, the one or more continuous queries 804 may be created tomodel business processing logic within ESPE 800, to predict eventswithin ESPE 800, to model a physical system within ESPE 800, to predictthe physical system state within ESPE 800, etc. For example, as noted,ESPE 800 may be used to support sensor data monitoring and management(e.g., sensing may include force, torque, load, strain, position,temperature, air pressure, fluid flow, chemical properties, resistance,electromagnetic fields, radiation, irradiance, proximity, acoustics,moisture, distance, speed, vibrations, acceleration, electricalpotential, or electrical current, etc.).

ESPE 800 may analyze and process events in motion or “event streams.”Instead of storing data and running queries against the stored data,ESPE 800 may store queries and stream data through them to allowcontinuous analysis of data as it is received. The one or more sourcewindows 806 and the one or more derived windows 808 may be created basedon the relational, pattern matching, and procedural algorithms thattransform the input event streams into the output event streams tomodel, simulate, score, test, predict, etc. based on the continuousquery model defined and application to the streamed data.

In an operation 906, a publish/subscribe (pub/sub) capability isinitialized for ESPE 800. In an illustrative embodiment, a pub/subcapability is initialized for each project of the one or more projects802. To initialize and enable pub/sub capability for ESPE 800, a portnumber may be provided. Pub/sub clients can use a host name of an ESPdevice running the ESPE and the port number to establish pub/subconnections to ESPE 800.

FIG. 10 is a block diagram of an ESP system 1000 interfacing betweenpublishing device 1022 and event subscription devices 1024 a-c accordingto some aspects. ESP system 1000 may include ESP subsystem 1001,publishing device 1022, an event subscription device A 1024 a, an eventsubscription device B 1024 b, and an event subscription device C 1024 c.Input event streams are output to ESP subsystem 1001 by publishingdevice 1022. In alternative embodiments, the input event streams may becreated by a plurality of publishing devices. The plurality ofpublishing devices further may publish event streams to other ESPdevices. The one or more continuous queries instantiated by ESPE 800 mayanalyze and process the input event streams to form output event streamsoutput to event subscription device A 1024 a, event subscription deviceB 1024 b, and event subscription device C 1024 c. ESP system 1000 mayinclude a greater or a fewer number of event subscription devices ofevent subscription devices.

Publish-subscribe is a message-oriented interaction paradigm based onindirect addressing. Processed data recipients specify their interest inreceiving information from ESPE 800 by subscribing to specific classesof events, while information sources publish events to ESPE 800 withoutdirectly addressing the receiving parties. ESPE 800 coordinates theinteractions and processes the data. In some cases, the data sourcereceives confirmation that the published information has been receivedby a data recipient.

A publish/subscribe API may be described as a library that enables anevent publisher, such as publishing device 1022, to publish eventstreams into ESPE 800 or an event subscriber, such as event subscriptiondevice A 1024 a, event subscription device B 1024 b, and eventsubscription device C 1024 c, to subscribe to event streams from ESPE800. For illustration, one or more publish/subscribe APIs may bedefined. Using the publish/subscribe API, an event publishingapplication may publish event streams into a running event streamprocessor project source window of ESPE 800, and the event subscriptionapplication may subscribe to an event stream processor project sourcewindow of ESPE 800.

The publish/subscribe API provides cross-platform connectivity andendianness compatibility between ESP application and other networkedapplications, such as event publishing applications instantiated atpublishing device 1022, and event subscription applications instantiatedat one or more of event subscription device A 1024 a, event subscriptiondevice B 1024 b, and event subscription device C 1024 c.

Referring back to FIG. 9, operation 906 initializes thepublish/subscribe capability of ESPE 800. In an operation 908, the oneor more projects 802 are started. The one or more started projects mayrun in the background on an ESP device. In an operation 910, an eventblock object is received from one or more computing device of thepublishing device 1022.

ESP subsystem 1001 may include a publishing client 1002, ESPE 800, asubscribing client A 1004, a subscribing client B 1006, and asubscribing client C 1008. Publishing client 1002 may be started by anevent publishing application executing at publishing device 1022 usingthe publish/subscribe API. Subscribing client A 1004 may be started byan event subscription application A, executing at event subscriptiondevice A 1024 a using the publish/subscribe API. Subscribing client B1006 may be started by an event subscription application B executing atevent subscription device B 1024 b using the publish/subscribe API.Subscribing client C 1008 may be started by an event subscriptionapplication C executing at event subscription device C 1024 c using thepublish/subscribe API.

An event block object containing one or more event objects is injectedinto a source window of the one or more source windows 806 from aninstance of an event publishing application on publishing device 1022.The event block object may be generated, for example, by the eventpublishing application and may be received by publishing client 1002. Aunique ID may be maintained as the event block object is passed betweenthe one or more source windows 806 and/or the one or more derivedwindows 808 of ESPE 800, and to subscribing client A 1004, subscribingclient B 1006, and subscribing client C 1008 and to event subscriptiondevice A 1024 a, event subscription device B 1024 b, and eventsubscription device C 1024 c. Publishing client 1002 may furthergenerate and include a unique embedded transaction ID in the event blockobject as the event block object is processed by a continuous query, aswell as the unique ID that publishing device 1022 assigned to the eventblock object.

In an operation 912, the event block object is processed through the oneor more continuous queries 804. In an operation 914, the processed eventblock object is output to one or more computing devices of the eventsubscription devices 1024 a-c. For example, subscribing client A 1004,subscribing client B 1006, and subscribing client C 1008 may send thereceived event block object to event subscription device A 1024 a, eventsubscription device B 1024 b, and event subscription device C 1024 c,respectively.

ESPE 800 maintains the event block containership aspect of the receivedevent blocks from when the event block is published into a source windowand works its way through the directed graph defined by the one or morecontinuous queries 804 with the various event translations before beingoutput to subscribers. Subscribers can correlate a group of subscribedevents back to a group of published events by comparing the unique ID ofthe event block object that a publisher, such as publishing device 1022,attached to the event block object with the event block ID received bythe subscriber.

In an operation 916, a determination is made concerning whether or notprocessing is stopped. If processing is not stopped, processingcontinues in operation 910 to continue receiving the one or more eventstreams containing event block objects from the, for example, one ormore network devices. If processing is stopped, processing continues inan operation 918. In operation 918, the started projects are stopped. Inoperation 920, the ESPE is shutdown.

As noted, in some examples, big data is processed for an analyticsproject after the data is received and stored. In other examples,distributed applications process continuously flowing data in real-timefrom distributed sources by applying queries to the data beforedistributing the data to geographically distributed recipients. Asnoted, an event stream processing engine (ESPE) may continuously applythe queries to the data as it is received and determines which entitiesreceive the processed data. This allows for large amounts of data beingreceived and/or collected in a variety of environments to be processedand distributed in real time. For example, as shown with respect to FIG.2, data may be collected from network devices that may include deviceswithin the internet of things, such as devices within a home automationnetwork. However, such data may be collected from a variety of differentresources in a variety of different environments. In any such situation,embodiments of the present technology allow for real-time processing ofsuch data.

Aspects of the present disclosure provide technical solutions totechnical problems, such as computing problems that arise when an ESPdevice fails which results in a complete service interruption andpotentially significant data loss. The data loss can be catastrophicwhen the streamed data is supporting mission critical operations, suchas those in support of an ongoing manufacturing or drilling operation.An example of an ESP system achieves a rapid and seamless failover ofESPE running at the plurality of ESP devices without serviceinterruption or data loss, thus significantly improving the reliabilityof an operational system that relies on the live or real-time processingof the data streams. The event publishing systems, the event subscribingsystems, and each ESPE not executing at a failed ESP device are notaware of or effected by the failed ESP device. The ESP system mayinclude thousands of event publishing systems and event subscribingsystems. The ESP system keeps the failover logic and awareness withinthe boundaries of out-messaging network connector and out-messagingnetwork device.

In one example embodiment, a system is provided to support a failoverwhen event stream processing (ESP) event blocks. The system includes,but is not limited to, an out-messaging network device and a computingdevice. The computing device includes, but is not limited to, one ormore processors and one or more computer-readable mediums operablycoupled to the one or more processor. The processor is configured toexecute an ESP engine (ESPE). The computer-readable medium hasinstructions stored thereon that, when executed by the processor, causethe computing device to support the failover. An event block object isreceived from the ESPE that includes a unique identifier. A first statusof the computing device as active or standby is determined. When thefirst status is active, a second status of the computing device as newlyactive or not newly active is determined. Newly active is determinedwhen the computing device is switched from a standby status to an activestatus. When the second status is newly active, a last published eventblock object identifier that uniquely identifies a last published eventblock object is determined. A next event block object is selected from anon-transitory computer-readable medium accessible by the computingdevice. The next event block object has an event block object identifierthat is greater than the determined last published event block objectidentifier. The selected next event block object is published to anout-messaging network device. When the second status of the computingdevice is not newly active, the received event block object is publishedto the out-messaging network device. When the first status of thecomputing device is standby, the received event block object is storedin the non-transitory computer-readable medium.

FIG. 11 flow chart of an example of a process for predicting andadjusting computer functionality according to some aspects. Someexamples can include more operations than, fewer operations than,different operations than, or a different order of the operations shownin FIG. 11. Some examples can be implemented using any of the systemsand processes described with respect to FIGS. 1-10.

In block 1100, a processing device (e.g., of a monitoring system)receives prediction data 1102. The prediction data 1102 is a time serieswith data points spanning a future time-period. The prediction data 1102represents a prediction over the future-time period. Receiving theprediction data 1102 can involve obtaining the prediction data 1102 froma remote computing device, such as a server or a client device;obtaining the prediction data 1102 from a database; or generating theprediction data 1102.

The prediction data 1102 is created from a historical dataset. Thehistorical dataset includes one or more time series spanning a priortime period. A predictive technique can be applied to the historicaldataset to generate the prediction data 1102. One example of apredictive technique involves applying a model to the historicaldataset. Non-limiting examples of the model can include anautoregressive integrated moving average (ARIMA) model, an ARIMA modelwith exogenous variables (ARIMAX), an unobserved component model (UCM),an exponential smoothing model (ESM), or any combination of these.

In some examples, the prediction data 1102 indicates how a device (e.g.,a computer) will function during a future time-period. To generate theprediction data 1102, a predictive technique can be applied to ahistorical dataset, whereby the historical dataset indicates variousaspects of how the device operated during a prior time-period. Forexample, the historical dataset can indicate the device's processorusage, memory consumption, latency, bandwidth consumption, softwareexecution patterns, memory access patterns, temperature, powerconsumption, or any combination of these, during the prior time-period.The historical dataset can be gathered by sensors or computers coupledto the device.

In other examples, the prediction data 1102 indicates future demand foran item or future production of an item. For example, the predictiondata 1102 can represent a prediction of how many times computer softwarewill be downloaded during a future time-period. As another example, theprediction data 1102 can represent a prediction of how many times aproduct will be purchased during a future time-period. As yet anotherexample, the prediction data 1102 can represent a prediction of how muchof an item will be manufactured during a future time-period. As stillanother example, the prediction data 1102 can represent a prediction ofhow many times digital content will be streamed during a future-timeperiod.

In still other examples, the prediction data 1102 indicates futureconsumption of a resource. For example, the prediction data 1102 canrepresent a prediction of how much electrical power will be consumedduring a future time-period. As another example, the prediction data1102 can represent a prediction of how much water or food will beconsumed during a future time-period. As yet another example, theprediction data 1102 can represent a prediction of how much processingpower or memory will be consumed by one or more devices during a futuretime-period.

Often, the prediction data 1102 will have errors due to a variety ofissues with the historical dataset (e.g., its format, size, or length)and the predictive technique used. It can be desirable to eliminatethese errors before relying on the prediction data 1102.

In block 1104, the processing device determines if the prediction data1102 has at least one abnormal data-point pattern 1108. In someexamples, the processing device can receive one or more files 1106usable for identifying the abnormal data-point pattern(s) 1108 in theprediction data 1102. Each file can include program code defining one ormore abnormal data-point patterns to be detected in the prediction data1102. The processing device can interpret and/or execute the programcode in each file to identify an abnormal data-point pattern(s) 1108 inthe prediction data 1102.

One example of the program code is shown in FIG. 12. Referring now toFIG. 12, the program code includes a function configured to receivecertain input parameters and provide a certain output. In this example,the function is named “userfunc1” and can receive the input parameters:actual (e.g., the historical data), predict (e.g., the prediction),seasonality, fcstHorizon, sign, and fuzzyfactor. The function can usethese inputs to calculate a minimum value and a maximum value of theprediction data, identify whether the prediction data has a flat patternand, if so, identify what type of flat pattern is present in theprediction data. A flat pattern can occur when a certain number ofconsecutive data points have equal values. The function can then outputa value of 0 to indicate that the flat pattern is not present or a valueof 1 to indicate that the flat pattern is present in the predictiondata. One or more users can create any number and combination of fileswith program code defining functions to be executed by the processingdevice to identify abnormal data-point patterns in the prediction data1102, thereby providing customizability.

The processing device may use preprogrammed rules to identify theabnormal data-point pattern 1108. For example, the processing device canuse rules specifying that the processing device is to search for apositive flat pattern in the prediction data 1102. One example of apositive flat pattern is shown in shown in circle 1302 of FIG. 13. Thedata points 1306 to the left of the vertical line 1304 depict thehistorical dataset used to generate prediction data. The data points tothe right of the vertical line 1304 (shown to the circle 1302) are theprediction data. The shaded area 1308 surrounding the prediction datarepresents confidence limits corresponding to the prediction data. Inone such example, the historical dataset can represent processing-powerconsumption over time and the prediction data can represent a predictionof processing-power consumption over a future time-period. Because theprediction data has a positive flat pattern, while the historicaldataset has varying activity, the processing device may determine thatthe prediction data has an abnormal data-point pattern.

Additionally or alternatively, the processing device can use rulesspecifying that the processing device is to search for an improperinactivity pattern in the prediction data 1102. In one such example, theprocessing device can identify a historical dataset that was used togenerate the prediction data 1102. The historical dataset may representa pattern of software activity during a prior time period. Theprocessing device can analyze the historical dataset to determine thatthere was activity during a time period between Jan. 1, 2016 and Jan. 9,2016, while the prediction data 1102 predicts that there will beinactivity been January 1 and January 9 of a future year (e.g., 2019).As a result, the processing device can determine that the predictiondata 1102 has an abnormal data-point pattern.

Additionally or alternatively, the processing device can use rulesspecifying that the processing device is to search for an extremepattern in the prediction data 1102. In one such example, the processingdevice can identify a historical dataset that was used to generate theprediction data 1102. The historical dataset may represent a pattern ofbandwidth consumption during a prior time period. The processing devicecan analyze the historical dataset to determine an average value of itsdata points—e.g., that there was an average bandwidth consumption of 2Gigabytes (GB) during the prior time period. The processing device cancompare the average value corresponding to the historical dataset withanother average value of the data points in the prediction data 1102(e.g., 2.5 GB) to determine a difference between the two. If thedifference exceeds a predefined threshold amount (e.g., 200 MB), theprocessing device can determine that the prediction data 1102 has anabnormal data-point pattern.

While comparing the prediction data 1102 to a corresponding historicaldataset can be useful for identifying potential problems in theprediction data 1102, it may also be useful to analyze characteristicsof the historical dataset. So, in some examples, the processing devicecan additionally or alternatively analyze the historical dataset. Theprocessing device can analyze the historical dataset for one or moreabnormal data-point patterns, which may signal that the prediction data1102 derived from the historical dataset is unreliable.

For example, the processing device can use preprogrammed rules or files1106 to analyze the historical dataset for the abnormal data-pointpatterns. In one such example, the processing device can analyze thehistorical dataset to determine that the historical dataset includes atime series that is less than a threshold length (e.g., a thresholdnumber of data points). Such a time series can be referred to as a shorttime series, and may have an insufficient amount of data from which togenerate a reliable prediction. One example of a short time series isshown in FIG. 14, with circle 1402 encompassing the historical dataset.Since there is a small amount of data within the historical dataset, theresulting prediction data 1406 shown to the right of the vertical line1404 is flat and likely inaccurate.

Other examples can involve the processing device searching thehistorical dataset for other types of abnormal data-point patterns, suchas data-point patterns indicating that the historical dataset includesan inactive (e.g., retried) time series. One example of a process fordetermining whether a time series is inactive is shown in FIG. 15 anddescribed below. But other examples can include more operations than,fewer operations than, different operations than, or a different orderof the operations shown in FIG. 15.

Referring now to FIG. 15, in block 1502, the processing device analyzesa time series in the historical dataset to determine if a set of basicrules apply to the time series. The basic rules are a series ofconditions that enable the processing device to immediately determinewhether the time series is active or inactive, without having to performa deeper analysis. Below are some examples of the basic rules:

-   -   If the time series has no consecutive zeros (e.g., consecutive        data points with values of zero) at the end, then the time        series is active. In one such example, a time series        representing memory consumption may not have any consecutive        zeros at the end, which may indicate that the processor is still        actively using the computer memory to perform tasks. This, in        turn, may indicate that the memory and/or processor is properly        functioning.    -   If the time series has more than a season's (e.g., one year's)        worth of consecutive zeros, then the time series is inactive. In        one such example, a time series representing memory consumption        may have consecutive zeros for more than one week, which may        indicate that the processor has not actively used the computer        memory to perform tasks for a while. This, in turn, may indicate        a problem with the memory and/or processor.    -   If the time series only has data points with values of zero,        then the time series is inactive. In one such example, a time        series representing memory consumption may only have zeros,        which may indicate that the memory has never been used. This, in        turn, may indicate a problem with the memory and/or processor.    -   If there is insufficient data in the time series to make a        determination of whether the time series is active or inactive,        then treat the time series as active. In one such example, a        time series representing memory consumption may be too short to        make a determination of whether it is active or inactive. So,        the time series can be treated as active by default.

The processing device can determine whether the basic rules apply to thetime series by analyzing the characteristics (e.g., length, data-pointvalues, etc.) of the time series. If the processing device determinesthat the basic rules apply to the time series, the process can proceedto block 1508, where the processing device can apply the basic rules tothe time series to determine whether the time series is active orinactive.

If the processing device determines that the basic rules do not apply tothe time series, the process can proceed to block 1506, where theprocessing device can determine if the time series is intermittent. Anintermittent time series is a time series in which there are severaltime-intervals with data points having values of zeros. As oneparticular example, an intermittent time series can represent demand forsoftware that is ending its life cycle and for which there are sporadicperiods with no downloads.

The processing device can determine whether the time series isintermittent by analyzing the characteristics of the time series. If thetime series is not intermittent, the process can continue to block 1508,in which the processing device applies a profile method to determinewhether the time series is active or inactive. The profile method caninvolve generating a historical profile for the time series, whereby thehistorical profile is an average of the past N seasons of data in thetime series. An example of a historical profile 1806 is shown in graph1804 of FIG. 18. After the processing device generates the historicalprofile, the processing device can compare the prediction data,period-by-period, with the historical profile. If for any period, thehistorical profile is active (e.g., has data points with non-zerovalues) while the prediction data has more than a threshold number ofconsecutive zeros, then the prediction data can be identified as havingan abnormal data-point pattern, specifically an improper inactivitypattern. For example, as shown in FIG. 18, a portion of the predictiondata 1802 is overlaid on the historical profile 1806 in graph 1804.Since there are only a few consecutive zeros 1808 in the prediction data1802 overlapping with the active period in the historical profile 1806,the processing device can determine that there is not an abnormaldata-point pattern in the prediction data 1802. In contrast, FIG. 19depicts an example in which a prediction data 1902 is compared to ahistorical profile 1906 in graph 1904. Since there are consecutive zeros1908 throughout the entire active period in the historical profile 1906,the processing device can determine that there is an abnormal data-pointpattern in the prediction data 1902.

Referring back to FIG. 15, if the time series is not intermittent, theprocess can continue to block 1510, in which the processing device candetermine whether an accumulated version of the time-series is seasonal.The processing device can generate the “accumulated version of a timeseries” by accumulating values in the time series to a lower frequency(e.g., if the original time series has daily data, it can be accumulatedto monthly or quarterly data). The processing device can then applyclassical time-series decomposition to the accumulated version of thetime-series to decompose the accumulated version of the time-series intoits components parts, one of which can be a seasonal component. Theprocessing device can analyze the seasonal component to identify anyseasonality in the time series.

If the processing device determines that the accumulated version of thetime series is seasonal, the process can continue to block 1514, inwhich the processing device applies the basic rules and the profilemethod on the accumulated version of the time series to determinewhether it is active or inactive.

If the processing device determines that the accumulated version of thetime series is not seasonal, the process can continue to block 1516, inwhich the processing device applies an intermittent method on theoriginal time series to determine whether the original time-series isactive or inactive. The intermittent method can involve comparing thenumber of trailing zeros (e.g., consecutive data points with values ofzero at the end of a time series) in the time series to all of the otherportions of the time series that have consecutive zeros and applying thefollowing rules:

-   -   If the number of trailing zeros at the end of the time series is        greater than the number of consecutive zeros in each of the        other portions of the time series, then the time series is        inactive. One example of this is shown in FIG. 16, in which        there are more trailing zeros 1604 at the end of the time series        than there are consecutive zeros 1602 elsewhere in the time        series.    -   Otherwise, the time series in active. One example of this is        shown in FIG. 17, in which there are fewer trailing zeros 1704        at the end of the time series than there are consecutive zeros        1702 elsewhere in the time series.

While the above examples provide certain methods of identifying abnormaldata-point patterns in prediction data, other examples can involve othermethods of identifying abnormal data-point patterns in prediction data.For instance, in one example, the processing device can determine afirst average-value of some or all of the data points in the predictiondata, determine a second average-value of some or all of the data pointsin the historical dataset, and compare the first average-value to thesecond average-value. If there is a difference between the two thatexceeds a threshold amount, the processing device can determine that theprediction data has an abnormal data-point pattern. As another example,the processing device can compare a first data-point pattern occurringwithin certain timespan of the prediction data to a second data-pointpattern within the same timespan in the historical dataset to determineif a difference exists between the two. If so, the difference maysignify an abnormal data-point pattern in the prediction data. Theprocessing device can compare any number and combination ofcharacteristics of the prediction data to any number and combination ofcharacteristics the historical dataset to identify one or more abnormaldata-point patterns. Further, any number and combination of the methodsdescribed herein can be combined to identify one or more abnormaldata-point patterns in the prediction data.

In some examples, the processing device can identify one or moreabnormal data-point patterns in the prediction data by performing only asingle pass through the prediction data. This can involve segmenting theprediction data into windows of data and analyzing each respectivedata-window using all of the preprogrammed rules and files (e.g., files1106 of FIG. 11) to detect abnormal data-point patterns therein beforemoving onto the next data-window. This can prevent the processing devicefrom having to repeatedly search through all of the prediction data(e.g., separately for each individual rule or file), which cansignificantly cut down on processing cycles, memory accesses, and memoryusage when analyzing the prediction data, thereby significantlyimproving the performance of the monitoring system.

If the processing device detects at least one abnormal data-pointpattern associated with the prediction data, the processing device canflag the prediction data as being potentially erroneous. For example,the processing device can flag the prediction data in a graphical userinterface by displaying an icon or textual indicator proximate to theprediction data. This can enable a user can further assess theprediction data manually.

Referring back to FIG. 11, if the processing device determines that theprediction data 1102 does not include an abnormal data-point pattern,the process can end. Otherwise, the process can proceed to block 1110.At block 1110, the processing device can identify one or more overrideprocesses 1112 corresponding to the abnormal data-point pattern(s) 1108identified in block 1104. An override process is a process by which avalue of a data point in the prediction data 1102 is replaced with a newvalue in order to mitigate the effect of the abnormal data-point pattern1108 on the prediction data 1102. Examples of the an override processcan include overriding the value of a particular data point in theprediction data 1102 with (i) an average value of the last N data-pointspreceding the particular data point; (ii) an average value of all thedata-points in the historical dataset; (iii) a moving average; (iv) avalue determined by random walk; (v) a user defined value or a valuederived from a user-defined function; or (vi) any combination of these.

In some examples, the processing device can identify the overrideprocess(es) corresponding to the abnormal data-point pattern(s) 1108using a database. The database can include relationships betweenoverride processes and abnormal data-point patterns. The processingdevice can use the database to map the abnormal data-point pattern 1108to one or more corresponding override processes 1112.

In other examples, the processing device can use the files 1106 toidentify the override processes 1112. For example, the files 1106 caninclude program code for implementing one or more override processes.Each of the files 1106 may also include an indicator (e.g., a commentedstatement) specifying one or more abnormal data-point patterns that canbe corrected by the override process defined therein. The processingdevice can analyze some or all of the files 1106 to determine whichfile(s) correspond to the abnormal data-point pattern 1108.

While the above examples may involve the processing device automaticallyselecting the override process(es) 1112 associated with the abnormaldata-point pattern 1108, in other examples the user can specify theoverride process(es) 1112. For example, the processing device mayprovide a graphical user interface with a list of candidate overrideprocesses from which a user can manually select one or more overrideprocesses 1112 to be used to correct the abnormal data-point pattern1108. In some examples, the processing device can generate the list ofcandidate override processes based on the abnormal data-point pattern1108. For example, the processing device can use a database orpreprogrammed rules to determine one or more candidateoverride-processes that correspond to the abnormal data-point pattern.In other examples, the processing device can generate the list using adefault set of candidate override processes.

After identifying one or more override processes 1112 associated withthe prediction data 1102, the process can proceed to block 1114. Atblock 1114, the processing device generates a corrected version of theprediction data 1116 by applying the override process(es) 1112 to theprediction data 1102. More specifically, the processing device can applyan override process by executing one or more preprogrammed rules,functions, or program code associated with the override process. Forexample, the processing device can implement an override process byinterpreting or executing program code in the files 1106 (or otherwisepreprogrammed into the system) defining the override process. As anotherexample, the processing device can implement a user-defined overrideprocess by performing user-defined operations that make up the overrideprocess.

In some examples, the processing device can apply multiple overrideprocesses to the prediction data 1102 to generate the corrected versionof the prediction data 1116. The processing device can apply theoverride processes in a preset order. For example, the processing devicemay have identified multiple abnormal data-point patterns in block 1104.The processing device may have then identified a corresponding overrideprocess for each of the abnormal data-point patterns in block 1110.Since some override processes may impact other override processes, itmay be desirable to implement the override processes in a particularorder. To determine the particular order, the processing device canreference a data source (e.g., a configuration file) that specifies howthe override processes are to be applied. The processing device can thenapply the override processes in the order specified by the data source.

In block 1118, the processing device adjusts one or more computerparameters based on the corrected version of the prediction data 1116.The one or more computer parameters can be adjusted so as to avoid orreduce the impact of a computer failure. Examples of the computerparameters can include (i) how data is allocated among computerprocesses, (ii) a setting for a piece of software on the computer, (iii)what software is running on the computer, (iv) how data is accessed orretrieved by the computer, (v) what data is stored on the computer or astorage location of the data, (vi) or any combination of these.

As a particular example, the processing device can analyze thecharacteristics (e.g., the magnitudes, frequencies, etc.) of the datapoints in the corrected prediction data 1116 for certain anomalies, suchas unusual patterns and/or statistical outliers. These anomalies can beassociated with various types of computer failures. The processingdevice can detect these anomalies using preprogrammed rules or adatabase identifying the anomalies. If the processing device identifiesan anomaly in the corrected version of the prediction data 1116, theprocessing device can then determine a mitigation method associated witha computer failure related to the anomaly. For example, the processingdevice can access a database having mitigation methods correlated toanomalies and select therefrom a particular mitigation methodcorresponding to the anomaly. The mitigation method may include one ormore operations configured to avoid the computer failure related to theanomaly or reduce the impact of the computer failure. The processingdevice can then execute the mitigation method.

While FIG. 11 depicts a process being applied to a single set ofprediction data 1102 for simplicity, it will be appreciated that some orall of this process can be repeated for multiple, individual sets ofprediction data. For example, there may be hundreds of thousands ormillions of sets of prediction data, with each set of prediction datarepresenting a different predictions. The sets of prediction data mayneed to be analyzed for errors (e.g., abnormal data-point patterns) andanomalies. This type of large-scale data analysis would be impossible toperform manually. But some examples of the present disclosure cansignificantly simplify and expedite the analysis of large amounts ofdata to identify errors and anomalies, while producing more accurateresults than could be achieved manually (if at all). This may enablegreater autonomous control over computers to prevent failures.

The foregoing description of certain examples, including illustratedexamples, has been presented only for the purpose of illustration anddescription and is not intended to be exhaustive or to limit thedisclosure to the precise forms disclosed. Numerous modifications,adaptations, and uses thereof will be apparent to those skilled in theart without departing from the scope of the disclosure. And the examplesdisclosed herein can be combined or rearranged to yield additionalexamples.

In the previous description, for the purposes of explanation, specificdetails are set forth in order to provide a thorough understanding ofexamples of the technology. But various examples can be practicedwithout these specific details. The figures and description are notintended to be restrictive.

The previous description provides examples that are not intended tolimit the scope, applicability, or configuration of the disclosure.Rather, the previous description of the examples provides those skilledin the art with an enabling description for implementing an example.Various changes may be made in the function and arrangement of elementswithout departing from the spirit and scope of the technology as setforth in the appended claims.

Specific details are given in the previous description to provide athorough understanding of the examples. But the examples may bepracticed without these specific details. For example, circuits,systems, networks, processes, and other components can be shown ascomponents in block diagram form to prevent obscuring the examples inunnecessary detail. In other examples, well-known circuits, processes,algorithms, structures, and techniques may be shown without unnecessarydetail in order to avoid obscuring the examples.

Also, individual examples may have been described as a process that isdepicted as a flowchart, a flow diagram, a data flow diagram, astructure diagram, or a block diagram. Although a flowchart can describethe operations as a sequential process, many of the operations can beperformed in parallel or concurrently. In addition, the order of theoperations can be re-arranged. And a process can have more or feweroperations than are depicted in a figure. A process can correspond to amethod, a function, a procedure, a subroutine, a subprogram, etc. When aprocess corresponds to a function, its termination can correspond to areturn of the function to the calling function or the main function.

Systems depicted in some of the figures can be provided in variousconfigurations. In some examples, the systems can be configured as adistributed system where one or more components of the system aredistributed across one or more networks in a cloud computing system.

The invention claimed is:
 1. A system comprising: a processing device;and a memory device including instructions that are executable by theprocessing device for causing the processing device to: receiveprediction data representing a prediction, wherein the prediction dataforms a time series that spans a future time-period; receive a pluralityof files defining abnormal data-point patterns to be identified in theprediction data, wherein each file in the plurality of files includescustomizable program-code for identifying a respective abnormal patternof data-point values in the prediction data; automatically identify aplurality of abnormal data-point patterns in the prediction data byinterpreting and executing the customizable program-code in theplurality of files; automatically determine a plurality of overrideprocesses that correspond to the plurality of abnormal data-pointpatterns in response to identifying the plurality of abnormal data-pointpatterns in the prediction data, wherein the plurality of overrideprocesses are automatically determined using correlations between theplurality of abnormal data-point patterns and the plurality of overrideprocesses, and wherein an override process involves replacing a value ofat least one data point in the prediction data with another value thatis configured to mitigate an impact of an abnormal data-point pattern onthe prediction; automatically determine that the plurality of overrideprocesses are to be applied to the prediction data in a particularorder; automatically generate a corrected version of the prediction datain response to determining the plurality of override processes, whereinthe corrected version of the prediction data is generated by executingthe plurality of override processes in the particular order; andautomatically adjust one or more computer parameters based on thecorrected version of the prediction data.
 2. The system of claim 1,wherein the memory device further includes instructions that areexecutable by the processing device for causing the processing device todetermine the particular order based on a configuration file specifyingthe particular order.
 3. The system of claim 1, wherein the memorydevice further includes instructions that are executable by theprocessing device for causing the processing device to flag theprediction as potentially erroneous in response to identifying the atlast one plurality of abnormal data-point patterns in a historicaldataset used to generate the prediction data.
 4. The system of claim 1,wherein the memory device further includes instructions that areexecutable by the processing device for causing the processing device toautomatically identify the plurality of abnormal data-point patterns inthe prediction data by performing only a single pass through theprediction data.
 5. The system of claim 1, wherein the plurality ofabnormal data-point patterns includes a first abnormal data-pointpattern and a second abnormal data-point pattern, and wherein the memorydevice further includes instructions that are executable by theprocessing device for causing the processing device to: automaticallydetermine the second abnormal data-point pattern associated with theprediction data by analyzing characteristics of a historical datasetused to generate the prediction data; automatically determine that thefirst abnormal data-point pattern corresponds to a firstoverride-process and the second abnormal data-point pattern correspondsto a second override-process using the correlations; and automaticallygenerate the corrected version of the prediction data at least in partby executing the second override-process in the particular order withrespect to the first override-process.
 6. The system of claim 5, whereinthe memory device further includes instructions that are executable bythe processing device for causing the processing device to automaticallydetermine the second abnormal data-point pattern by: determining thatthe historical dataset used to generate the prediction data has a seriesof consecutive data points with values of zero spanning at least anentire season cycle within the historical dataset.
 7. The system ofclaim 5, wherein the memory device further includes instructions thatare executable by the processing device for causing the processingdevice to automatically determine the second abnormal data-point patternby: determining that an end of the prediction data has consecutivedata-points with values of zero; determining whether any timespan in thehistorical dataset that was used to generate the prediction dataincludes at least as many consecutive data-points with values of zero asare at the end of the prediction data; and detecting the second abnormaldata-point pattern in response to determining that there is no timespanin the historical dataset that includes at least as many consecutivedata-points with values of zero as are at the end of the predictiondata.
 8. The system of claim 5, wherein the memory device furtherincludes instructions that are executable by the processing device forcausing the processing device to automatically determine that theprediction data has the second abnormal data-point pattern by:generating a historical profile for a timespan of the historical datasetthat was used to generate the prediction data, wherein the historicalprofile is an average of multiple seasons of information within thehistorical dataset; determining that the timespan in the historicalprofile is an active period in which there are no consecutivedata-points with values of zero; analyzing the same timespan in theprediction to determine that the timespan in the prediction data is aninactive period in which there are consecutive data-points with valuesof zero; and determining that the prediction data has the secondabnormal data-point pattern in response to determining that the timespanrepresents an active period in the historical profile and an inactiveperiod in the prediction data.
 9. The system of claim 5, wherein thememory device further includes instructions that are executable by theprocessing device for causing the processing device to automaticallydetermine that the prediction data has the second abnormal data-pointpattern by: determining an average value of the prediction data;determining another average value of the historical dataset that wasused to generate the prediction data; determining that the average valueof the prediction data is different from the other average value of thehistorical dataset by more than a threshold amount; and determining thatthe prediction data has the second abnormal data-point pattern inresponse to determining that the average value of the prediction data isdifferent from the other average value of the historical dataset by morethan the threshold amount.
 10. The system of claim 5, wherein the memorydevice further includes instructions that are executable by theprocessing device for causing the processing device to automaticallydetermine that the prediction data has the second abnormal data-pointpattern by: automatically comparing (i) a data-point pattern during atime period in the prediction data to (ii) another data-point patternduring the time period in the historical dataset that was used togenerate the prediction data in order to determine a difference betweenthe data-point patterns; and automatically determining that thedifference between the data-point patterns is indicative of the secondabnormal data-point pattern in the prediction data.
 11. A non-transitorycomputer-readable medium comprising program code that is executable by aprocessing device for causing the processing device to: receiveprediction data representing a prediction, wherein the prediction dataforms a time series that spans a future time-period; receive a pluralityof files defining abnormal data-point patterns to be identified in theprediction data, wherein each file in the plurality of files includescustomizable program-code for identifying a respective abnormal patternof data-point values in the prediction; automatically identify aplurality of abnormal data-point patterns in the prediction data byinterpreting and executing the customizable program-code in theplurality of files; automatically determine a plurality of overrideprocesses that correspond to the plurality of abnormal data-pointpatterns in response to identifying the plurality of abnormal data-pointpatterns in the prediction data, wherein the plurality of overrideprocesses are automatically determined using correlations between theplurality of abnormal data-point patterns and the plurality of overrideprocesses, and wherein an override process involves replacing a value ofat least one data point in the prediction data with another value thatis configured to mitigate an impact of an abnormal data-point pattern onthe prediction; automatically determine that the plurality of overrideprocesses are to be applied to the prediction data in a particularorder; automatically generate a corrected version of the prediction datain response to determining the plurality of override processes, whereinthe corrected version of the prediction data is generated by executingthe plurality of override processes in the particular order; andautomatically adjust one or more computer parameters based on thecorrected version of the prediction data.
 12. The non-transitorycomputer-readable medium of claim 11, further comprising program codethat is executable by the processing device for causing the processingdevice to determine the particular order based on a configuration filespecifying the particular order.
 13. The non-transitorycomputer-readable medium of claim 11, further comprising program codethat is executable by the processing device for causing the processingdevice to flag the prediction data as potentially erroneous in responseto identifying the plurality of abnormal data-point patterns in ahistorical dataset that was used to generate the prediction data. 14.The non-transitory computer-readable medium of claim 11, furthercomprising program code that is executable by the processing device forcausing the processing device to automatically identify the plurality ofabnormal data-point patterns in the prediction data by performing only asingle pass through the prediction data.
 15. The non-transitorycomputer-readable medium of claim 11, wherein the plurality of abnormaldata-point patterns includes a first abnormal data-point pattern and asecond abnormal data-point pattern, and further comprising program codethat is executable by the processing device for causing the processingdevice to: automatically determine the second abnormal data-pointpattern associated with the prediction data by analyzing characteristicsof a historical dataset used to generate the prediction data;automatically determine that the first abnormal data-point patterncorresponds to a first override-process and the second abnormaldata-point pattern corresponds to a second override-process using thecorrelations; and automatically generate the corrected version of theprediction data at least in part by executing the secondoverride-process in the particular order with respect to the firstoverride-process.
 16. The non-transitory computer-readable medium ofclaim 15, further comprising program code that is executable by theprocessing device for causing the processing device to automaticallydetermine the second abnormal data-point pattern by: determining thatthe historical dataset that was used to generate the prediction data hasa series of consecutive data points with values of zero spanning atleast an entire season cycle within the historical dataset.
 17. Thenon-transitory computer-readable medium of claim 15, further comprisingprogram code that is executable by the processing device for causing theprocessing device to automatically determine the second abnormaldata-point pattern by: determining that an end of the prediction datahas consecutive data-points with values of zero; determining whether anytimespan in the historical dataset that was used to generate theprediction data includes at least as many consecutive data-points withvalues of zero as are at the end of the prediction data; and detectingthe second abnormal data-point pattern in response to determining thatthere is no timespan in the historical dataset that includes at least asmany consecutive data-points with values of zero as are at the end ofthe prediction data.
 18. The non-transitory computer-readable medium ofclaim 15, further comprising program code that is executable by theprocessing device for causing the processing device to automaticallydetermine that the prediction has the second abnormal data-point patternby: generating a historical profile for a timespan of the historicaldataset that was used to generate the prediction data, wherein thehistorical profile is an average of multiple seasons of informationwithin the historical dataset; determining that the timespan in thehistorical profile is an active period in which there are no consecutivedata-points with values of zero; analyzing the same timespan in theprediction data to determine that the timespan in the prediction data isan inactive period in which there are consecutive data-points withvalues of zero; and determining that the prediction data has the secondabnormal data-point pattern in response to determining that the timespanrepresents an active period in the historical profile and an inactiveperiod in the prediction data.
 19. The non-transitory computer-readablemedium of claim 15, further comprising program code that is executableby the processing device for causing the processing device toautomatically determine that the prediction data has the second abnormaldata-point pattern by: determining an average value of the predictiondata; determining another average value of the historical dataset thatwas used to generate the prediction data; determining that the averagevalue of the prediction data is different from the other average valueof the historical dataset by more than a threshold amount; anddetermining that the prediction data has the second abnormal data-pointpattern in response to determining that the average value of theprediction data is different from the other average value of thehistorical dataset by more than the threshold amount.
 20. Thenon-transitory computer-readable medium of claim 15, further comprisingprogram code that is executable by the processing device for causing theprocessing device to automatically determine that the prediction has thesecond abnormal data-point pattern by: automatically comparing (i) adata-point pattern during a time period in the prediction data to (ii)another data-point pattern during the time period in the historicaldataset that was used to generate the prediction data in order todetermine a difference between the data-point patterns; andautomatically determining that the difference between the data-pointpatterns is indicative of the second abnormal data-point pattern in theprediction data.
 21. A method comprising: receiving, by a processingdevice, prediction data representing a prediction, wherein theprediction data forms a time series that spans a future time-period;receiving, by the processing device, a plurality of files definingabnormal data-point patterns to be identified in the prediction data,wherein each file in the plurality of files includes customizableprogram-code for identifying a respective abnormal pattern of data-pointvalues in the prediction data; automatically identifying, by theprocessing device, a plurality of abnormal data-point patterns in theprediction data by interpreting and executing the customizableprogram-code in the plurality of files; automatically determining, bythe processing device, a plurality of override processes that correspondto the plurality of abnormal data-point patterns in response toidentifying the plurality of abnormal data-point patterns in theprediction data, wherein the plurality of override processes areautomatically determined using correlations between the plurality ofabnormal data-point patterns and the plurality of override processes,and wherein an override process involves replacing a value of at leastone data point in the prediction data with another value that isconfigured to mitigate an impact of an abnormal data-point pattern onthe prediction; automatically determining, by the processing device,that the plurality of override processes are to be applied to theprediction data in a particular order; automatically generating, by theprocessing device, a corrected version of the prediction data inresponse to determining the plurality of override processes, wherein thecorrected version of the prediction data is generated by executing theplurality of override processes in the particular order; andautomatically adjusting, by the processing device, one or more computerparameters based on the corrected version of the prediction data. 22.The method of claim 21, further comprising determining the particularorder based on a configuration file specifying the particular order. 23.The method of claim 21, further comprising flagging the prediction aspotentially erroneous in response to identifying the plurality ofabnormal data-point patterns in a historical dataset used to generatethe prediction data.
 24. The method of claim 21, further comprisingautomatically identifying the plurality of abnormal data-point patternsin the prediction data by performing only a single pass through theprediction data.
 25. The method of claim 21, wherein the plurality ofabnormal data-point patterns includes a first abnormal data-pointpattern and a second abnormal data-point pattern, and furthercomprising: automatically determining the second abnormal data-pointpattern associated with the prediction data by analyzing characteristicsof a historical dataset used to generate the prediction data;automatically determining that the first abnormal data-point patterncorresponds to a first override-process and the second abnormaldata-point pattern corresponds to a second override-process using thecorrelations; and automatically generating the corrected version of theprediction data at least in part by executing the secondoverride-process in the particular order with respect to the firstoverride-process.
 26. The method of claim 25, further comprisingautomatically determining the second abnormal data-point pattern by:determining that the historical dataset that was used to generate theprediction data has a series of consecutive data points with values ofzero spanning at least an entire season cycle within the historicaldataset.
 27. The method of claim 25, further comprising automaticallydetermining the second abnormal data-point pattern by: determining thatan end of the prediction data has consecutive data-points with values ofzero; determining whether any timespan in the historical dataset thatwas used to generate the prediction data includes at least as manyconsecutive data-points with values of zero as are at the end of theprediction data; and detecting the second abnormal data-point pattern inresponse to determining that there is no timespan in the historicaldataset that includes at least as many consecutive data-points withvalues of zero as are at the end of the prediction data.
 28. The methodof claim 25, further comprising automatically determining that theprediction data has the second abnormal data-point pattern by:generating a historical profile for a timespan of the historical datasetthat was used to generate the prediction data, wherein the historicalprofile is an average of multiple seasons of information within thehistorical dataset; determining that the timespan in the historicalprofile is an active period in which there are no consecutivedata-points with values of zero; analyzing the same timespan in theprediction data to determine that the timespan in the prediction data isan inactive period in which there are consecutive data-points withvalues of zero; and determining that the prediction data has the secondabnormal data-point pattern in response to determining that the timespanrepresents an active period in the historical profile and an inactiveperiod in the prediction.
 29. The method of claim 25, further comprisingautomatically determining that the prediction has the second abnormaldata-point pattern by: determining an average value of the predictiondata; determining another average value of the historical dataset thatwas used to generate the prediction data; determining that the averagevalue of the prediction data is different from the other average valueof the historical dataset by more than a threshold amount; anddetermining that the prediction data has the second abnormal data-pointpattern in response to determining that the average value of theprediction data is different from the other average value of thehistorical dataset by more than the threshold amount.
 30. The method ofclaim 25, further comprising automatically determining that theprediction has the second abnormal data-point pattern by: automaticallycomparing (i) a data-point pattern during a time period in theprediction data to (ii) another data-point pattern during the timeperiod in the historical dataset that was used to generate theprediction data in order to determine a difference between thedata-point patterns; and automatically determining that the differencebetween the data-point patterns is indicative of the second abnormaldata-point pattern in the prediction data.